Introduction: Function calling transforms LLMs from text generators into action-taking agents. Instead of just producing text responses, models can now decide when to call external functions, APIs, or tools to accomplish tasks. This capability enables building assistants that can search the web, query databases, send emails, execute code, and interact with any system that exposes […]
Read more →Category: Artificial Intelligence(AI)
Introducing Azure IoT Edge
During Build! 2017 Microsoft has announced the availability of Azure IoT Edge, which would bring in some of the cloud capabilities to edge devices/networks within your Enterprise. This would enable industrial devices to utilize the capabilities of IoT in Azure within their constrained resources . With this Microsoft now makes it easier for developers to […]
Read more →LLM Output Parsing: Transforming Unstructured Text into Reliable Data Structures
Introduction: LLM outputs are inherently unstructured—models generate text, not data structures. Yet most applications need structured data: JSON for APIs, typed objects for business logic, specific formats for downstream processing. Output parsing bridges this gap, transforming free-form text into reliable, validated data structures. This guide covers the techniques that make parsing robust: format specification in […]
Read more →Advanced RAG Patterns: From Query Rewriting to Self-Reflective Retrieval
Introduction: Basic RAG retrieves documents and stuffs them into context. Advanced RAG transforms retrieval into a sophisticated pipeline that dramatically improves answer quality. This guide covers the techniques that separate production RAG systems from prototypes: query rewriting to improve retrieval, hybrid search combining dense and sparse methods, cross-encoder reranking for precision, contextual compression to fit […]
Read more →LLM Deployment Strategies: From Model Optimization to Production Scaling
Introduction: Deploying LLMs to production is fundamentally different from deploying traditional ML models. The models are massive, inference is computationally expensive, and latency requirements are stringent. This guide covers the strategies that make LLM deployment practical: model optimization techniques like quantization and pruning, inference serving with batching and caching, containerization with GPU support, auto-scaling based […]
Read more →Multimodal AI Applications: Building Systems That See, Hear, and Understand
Introduction: Multimodal AI processes and generates content across multiple modalities—text, images, audio, and video. This capability enables applications that were previously impossible: describing images, generating images from text, transcribing and understanding audio, and creating unified experiences that combine all these modalities. This guide covers the practical aspects of building multimodal applications: vision-language models for image […]
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